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library(tidyverse)
## Warning in as.POSIXlt.POSIXct(Sys.time()): unknown timezone 'zone/tz/2019c.1.0/
## zoneinfo/America/New_York'
library(ggplot2)
library(RColorBrewer)
library(plotly)
library(DT)
library(dplyr)
SNPs <- read.table("23andMe_complete.txt", header = T, sep = "\t")
ggplot(SNPs, aes(chromosome)) +
geom_bar(colour="darkblue", fill="blue")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs") +
xlab("Chromosome")
#CHR sort
SNPs$chromosome = ordered(SNPs$chromosome, levels=c(seq(1, 22), "X", "Y", "MT"))
ggplot(SNPs, aes(chromosome)) +
geom_bar(colour="darkblue", fill="blue")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs") +
xlab("Chromosome")
CBP <- c("AA"="#999999","A"="#E69F00","AC"="#56B4E9","AG"="#009E73","AT"="#F0E442","C"="#0072B2","CC"="#D55E00","CG"="#CC79A7","CT"="#000000","D"="#FFCC00","DD"="#FF9900","DI"="#FF3300","G"="#99CC00","GG"="#33FF00","GT"="#00FFCC","I"="#9900FF","II"="#666699","T"="#CCFFCC","TT"="#996699","--"="#00CCFF")
ggplot(SNPs, aes(chromosome, fill=genotype)) +
geom_bar(color="white")+
scale_fill_manual(values = CBP)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs") +
xlab("Chromosome")
ppi <- 400
png("SNP_plot.png", width=6*ppi, height=6*ppi, res=ppi)
ggplot(SNPs, aes(chromosome, fill=genotype)) +
geom_bar(color="black", position="dodge")+
scale_fill_manual(values = CBP, name="Genotype")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs") +
xlab("Chromosome")
dev.off()
## quartz_off_screen
## 2
Genotype counts per chromosome
ggplot(SNPs, aes(genotype, fill=genotype)) +
geom_bar(color="black", position="dodge")+
scale_fill_manual(values = CBP, name="Genotype")+
facet_wrap( ~ chromosome , ncol = 5)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank())+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs")
#log2 trans
ggplot(SNPs, aes(genotype, fill=genotype)) +
geom_bar(color="black", position="dodge")+
scale_fill_manual(values = CBP, name="Genotype")+
facet_wrap( ~ chromosome , ncol = 5)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank())+
scale_y_continuous(trans = 'log2')+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs")
p <- ggplot(SNPs, aes(genotype, fill=genotype)) +
geom_bar(color="black", position="dodge")+
scale_fill_manual(values = CBP, name="Genotype")+
facet_wrap( ~ chromosome , ncol = 5)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank())+
scale_y_continuous(trans = 'log2')+
ggtitle("Total SNPs for each genotype") +
ylab("Total number of SNPs")
ggplotly(p)
#library(dplyr)
SNP_Y <- filter(SNPs, chromosome == "Y")
datatable(SNP_Y)
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html